# A Reality Check of Inference-Time Context Augmentation Methods

Authors: Haeun Yu, Geert Heyman, Pepa Atanasova, Isabelle Augenstein.

Tests inserting blank filler, repeated or rephrased sentences, and generated explanations at different points in a prompt. Tracks both corrected and newly broken answers across seven models and three datasets, showing why average accuracy alone can hide unreliable gains.

First public: 2026-10-22.
ArXiv initial submission: 2026-10-22.

[Original paper](https://arxiv.org/abs/2610.30983) · [PDF](https://arxiv.org/pdf/2610.30983) · [HTML](https://arxiv.org/html/2610.30983v1) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.30983/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.30983/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.30983/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.30983/record.json)

## Approaches

- [Thinking tokens](https://latent-implicit-thinking.github.io/topics/thinking-tokens/): Give a model extra tokens for computation, such as pauses, filler symbols, or learned planning tokens. These tokens need not spell out the solution.
- [Analysis and evaluation](https://latent-implicit-thinking.github.io/topics/implicit-reasoning-evaluation/): Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.

Reading-list version 0.11.0; literature checked through 2026-10-26.